Robust localization system using online / offline hybrid learning

In this paper, we propose an online motion model parameter estimation method. To achieve accurate localization, accurate estimation of motion model parameters is needed. However, the true values of motion model parameters change sequentially according to alteration of surrounding environments. Therefore the online estimation is absolutely imperative. As a typical method to estimate motion model parameters sequentially, Augmented Kalman Filter (AKF) is there. AKF achieves parameter estimation through Kalman filtering algorithm. However, AKF has serious problems to be implemented in real robot operation. These problems are the accuracy of observation and the limitation to motion control of robots. To solve these problems and achieve accurate motion model parameter estimation, proposed method introduces discriminative training. The introduction of discriminative training increases the convergence performance and stability of parameter estimation through AKF. The proposal method achieves accurate motion model parameter estimation in real robot operation. This paper describes the efficiency of our technique through simulations and an outdoor experiment.